Daily Assignment 21

Ecosystem Science and Sustainability 330

library(dataRetrieval)
library(dplyr)

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

library(tsibble)

Registered S3 method overwritten by 'tsibble':
  method               from 
  as_tibble.grouped_df dplyr

Attaching package: 'tsibble'

The following objects are masked from 'package:base':

    intersect, setdiff, union

# Example: Cache la Poudre River at Mouth (USGS site 06752260)
poudre_flow <- readNWISdv(siteNumber = "06752260",    # Download data from USGS for site 06752260
                          parameterCd = "00060",      # Parameter code 00060 = discharge in cfs)
                          startDate = "2013-01-01",   # Set the start date
                          endDate = "2023-12-31") |>  # Set the end date
  renameNWISColumns() |>                              # Rename columns to standard names (e.g., "Flow", "Date")
  mutate(Date = yearmonth(Date)) |>                   # Convert daily Date values into a year-month format (e.g., "2023 Jan")
  group_by(Date) |>                                   # Group the data by the new monthly Date
  summarise(Flow = mean(Flow))                       # Calculate the average daily flow for each month

# 1. Convert to tsibble
poudre_ts <- poudre_flow %>%
  as_tsibble(index = Date)

poudre_ts

# A tsibble: 132 x 2 [1M]
       Date    Flow
      <mth>   <dbl>
 1 2013 Jan   18.1 
 2 2013 Feb   18.0 
 3 2013 Mar    8.21
 4 2013 Apr    5.94
 5 2013 May  333.  
 6 2013 Jun  300.  
 7 2013 Jul   75.6 
 8 2013 Aug   48.8 
 9 2013 Sep 1085.  
10 2013 Oct  146.  
# ℹ 122 more rows

# 2. Plotting the time series
library(ggplot2)
library(plotly)

Attaching package: 'plotly'

The following object is masked from 'package:ggplot2':

    last_plot

The following object is masked from 'package:stats':

    filter

The following object is masked from 'package:graphics':

    layout

flowplot <- ggplot(poudre_ts, aes(x = Date, y = Flow)) +
  geom_line() +
  labs(title = "Monthly Mean Discharge\nCache la Poudre River (2013–2023)",
       x = "Year‑Month", y = "Discharge (cfs)")

print(flowplot)

ggplotly(flowplot)

# 3. Subseries
library(feasts)

Loading required package: fabletools

poudre_ts %>%
  gg_subseries(Flow) +
  labs(title = "Subseries Plot of Monthly Flow",
       y = "Discharge (cfs)")

In this plot, the “seasons” are the calendar months (January to December), with each little line as the monthly totals plotted over the years. The subseries allows you to compare how the flows varied over the years from 2013-2023 by individual months. From the plot, it is seen that there are peaks in late spring/early summer and lows in winter, revealing the seasonal cycle of snow melt runoff.

# 4. Decompose
library(fable)
stl_mod <- poudre_ts %>%
  model(
    STL(Flow ~ trend(window = 13) + season(window = "periodic"))
  )
components <- stl_mod %>% components()

autoplot(components) +
  labs(title = "STL Decomposition of Monthly Flow")

In this plot, the trend is a slowly varying line showing whether baseline flows are rising/falling over the decade. Seasonally, there is a repeating annual cycle, where it peaks in the spring, then troughs in the winter. I think the trend represents the basin’s baseline discharge, such as changes in the climate, land, or water-use policies. The seasonal pattern is driven by the hydrological cycle, where snow accumulates through the winter, then melts rapidly in late spring.